637 citations
- University College LondonGB90 papers
- Centre National de la Recherche ScientifiqueFR8 papers
- Sapienza University of RomeIT6 papers
- Imperial College LondonGB5 papers
- University of OxfordGB5 papers
- European Organization for Nuclear ResearchCH4 papers
- Georgia Institute of TechnologyUS4 papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR4 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di BolognaIT4 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di PisaIT4 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma IIT4 papers
- Ludwig-Maximilians-Universität MünchenDE4 papers
5 papers · 1 filter
Efficient Proxy Raytracer for Optical Systems using Implicit Neural Representations
Shiva Sinaei, Chuanjun Zheng, Kaan Akşit +1
Ray tracing is a widely used technique for modeling optical systems, involving sequential surface-by-surface computations, which can be computationally intensive. We propose Ray2Ra…
Physics-informed Deep Learning to Solve Three-dimensional Terzaghi Consolidation Equation: Forward and Inverse Problems
Biao Yuan, Ana Heitor, He Wang +1
The emergence of neural networks constrained by physical governing equations has sparked a new trend in deep learning research, which is known as Physics-Informed Neural Networks (…
A Note on the Convergence of Denoising Diffusion Probabilistic Models
Sokhna Diarra Mbacke, Omar Rivasplata
Diffusion models are one of the most important families of deep generative models. In this note, we derive a quantitative upper bound on the Wasserstein distance between the data-g…
Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction
Xiaowei Gao, Xinke Jiang, Dingyi Zhuang +4
Traffic accidents present substantial challenges to human safety and socio-economic development in urban areas. Developing a reliable and responsible traffic accident prediction mo…
Rician likelihood loss for quantitative MRI using self-supervised deep learning
Christopher S. Parker, Anna Schroder, Sean C. Epstein +3
Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the cho…